The modern communications era has brought about a tremendous expansion of wireline and wireless networks. Computer networks, television networks, and telephony networks are experiencing an unprecedented technological expansion, fueled by consumer demand. Wireless and mobile networking technologies have addressed related consumer demands, while providing more flexibility and immediacy of information transfer.
Current and future networking technologies continue to facilitate ease of information transfer and convenience to users. Due to the now ubiquitous nature of electronic communication devices, people of all ages and education levels are utilizing electronic devices to communicate with other individuals or contacts, receive services and/or share information, media and other content. One area in which there is a demand to increase ease of information transfer relates to services for performing image processing. In this regard, for example, improving the reliability of accurately determining a pose of one or more objects in an image or video may enhance image processing.
At present, a pose of a detected object may be performed by pose based object detectors. Although a conventional pose based object detector may detect and classify a specific pose of an object, the chance of conventional pose based object detectors detecting a pose incorrectly may be higher than desirable. This is typically because training of a classifier of a pose is generally performed by using many samples of a same pose in which the variations in the samples may be huge due to irregularities in cropping, and/or feature selection may not be robust enough to effectively represent a pose of an object.
In particular, conventional pose based object detectors may encounter problems in pose detection in instances associated with border faces, errors due to process of detection, cropping irregularities, etc. For instance, by utilizing conventional pose based detectors, the discrimination between two poses may typically be less at the border. For example, faces at the edge of an image may be more difficult to detect. As such, pose detection of these border faces utilizing conventional approaches often result in pose detection error. Additionally, conventional posed based detectors may encounter errors due to process of detection. For instance, if a scanning window encloses a partial frontal face, in an instance in which the scanning window should enclose the full face, the conventional pose based detectors oftentimes determine that a detected face corresponds to a half profile pose (e.g., yaw<45 degrees) instead of a frontal pose of the face, due to the view in the window being closer to a half profile than a frontal profile. This may be due to the inherent nature of window scanning. Moreover, by utilizing conventional pose based detectors, errors in pose detection may occur due to one or more cropping irregularities of an image.
In view of the foregoing drawbacks, it may be beneficial to provide a mechanism for efficiently and reliably determining a pose of one or more detected objects.